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pmid:31623626
DNA methylation exploration for ARDS: a multi-omics and multi-microarray interrelated analysis.
Shi Zhang · Zongsheng Wu · Jianfeng Xie · Yi Yang · Lei Wang · Haibo Qiu
Journal of translational medicine · 2019 doi:10.1186/s12967-019-2090-1
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31623626", "kind": "science-record", "revision": 7, "content_hash": "sha256:93a5c95154e110dd54bc7c745433111a5e0abf9a891b99b33fd1aff79070c9fc", "created_at": "2026-11-01T15:40:50Z", "updated_at": "2026-11-01T22:00:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "DNA methylation exploration for ARDS: a multi-omics and multi-microarray interrelated analysis.", "doi": "10.1186/s12967-019-2090-1", "pmid": "31623626", "authors": ["Shi Zhang", "Zongsheng Wu", "Jianfeng Xie", "Yi Yang", "Lei Wang", "Haibo Qiu"], "journal": "Journal of translational medicine", "year": 2019, "abstract": "BACKGROUND: Despite advances in clinical management, there are currently no novel therapeutic targets for acute respiratory distress syndrome (ARDS). DNA methylation, as a reversible process involved in the development and progression of many diseases, would be used as potential therapeutic targets to improve the treatment strategies of ARDS. However, the meaningful DNA methylation sites associated with ARDS still remain largely unknown. We sought to determine the difference in DNA methylation between ARDS patients and healthy participants, and simultaneously, the feasible DNA methylation markers for potential therapeutic targets were also explored.\nMETHODS: Microarray data of human blood samples for ARDS and healthy participants up to June 2019 was searched in GEO database. The difference analyses between ARDS and healthy population were performed through limma R package, and furthermore, interrelated analyses of DNA methylation and transcript were accomplished by VennDiagram R package. Perl and sva R package were used to merge microarray data and decrease heterogeneities among different studies. The biological function of screened methylation sites and their regulating genes were annotated according to UniProt database and Pubmed database. GO term and KEGG pathway enrichment analyses were conducted using DAVID 6.8 and KOBAS 3.0. The meaningful DNA methylation markers to distinguish ARDS from healthy controls were explored through ROC (receiver operating characteristic curves) analyses.\nRESULTS: Five datasets in GEO databases (one DNA methylation dataset, three mRNA datasets, and one mRNA dataset of healthy people) were enrolled in present analyses finally, and the series were GSE32707, GSE66890, GSE10474, GSE61672, and GSE67530. These databases included 99 patients with ARDS (within 48 h of onset) and 136 healthy participants. Difference analyses indicated 44,439 DNA methylation alterations and 29 difference mRNAs between ARDS and healthy controls. 40 methylation variations regulated transcription of 16 genes was explored via interrelated analysis. According to the functional annotations, 30 DNA methylation sites were related to the imbalance of inflammation or immunity, endothelial function, epithelial function and/or coagulation function. cg03341377, cg24310395, cg07830557 and cg08418670, with AUC up to 0.99, might be the meaningful characteristics with the highest performance to distinguish ARDS from healthy controls.\nCONCLUSIONS: 44,439 DNA methylation alterations and 29 difference mRNAs exist between ARDS and healthy controls. 30 DNA methylation sites may regulate transcription of 10 genes, which take part in pathogenesis of ARDS. These findings could be intervention targets, with validation experiments to be warranted to assess these further.", "claims": [{"id": "claim-method", "statement": "To identify novel methylation alterations which would to distinguish ARDS from healthy volunteers, the receiver operating characteristic curves (ROCs) were performed to calculate the area under the curve (AUC) on screened DNA methylation variations using ROCR R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31623626", "locator": "sections[7] \"Evaluation the value of methylation alterations to distinguish ARDS from healthy volunteers\" paragraph 2", "quote": "To identify novel methylation alterations which would to distinguish ARDS from healthy volunteers, the receiver operating characteristic curves (ROCs) were performed to calculate the area under the curve (AUC) on screened DNA methylation variations using ROCR R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated and/or analysed during this study can be found in GEO database. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE32707", "GSE66890", "GSE10474", "GSE61672", "GSE67530"], "resolved_accessions": ["GSE32707", "GSE66890", "GSE10474", "GSE61672", "GSE67530"], "statements": ["All data generated and/or analysed during this study can be found in GEO database. The series of these studies were GSE32707, GSE66890, GSE10474, GSE61672 and GSE67530."], "basis": "The paper names GSE32707, GSE66890, GSE10474, GSE61672, GSE67530 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. 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METHODS: Microarray data of human blood samples for ARDS and hea", "e. Perl and sva R package were used to merge microarray data and decrease heterogeneities among diff"], "order_basis": "paper_text_order:17", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["limma"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}